Figures for 3D Liver Print Systematic Scoping Review

This document provides code for importing and cleaning data from the systematic review that was used to create all figures provided in the submitted manuscript.

Figure 1 - Printing Methods

Figure 1a - Kind of printing method used

print_method <- reconciled %>%  
                  select(study_ID, 
                   `4.1 What kind of printing method is used?_ae684e28-4f90-44ad-9de2-731d076de0b0_Answer`) %>% 
                  separate_rows(`4.1 What kind of printing method is used?_ae684e28-4f90-44ad-9de2-731d076de0b0_Answer`, sep="\\|")

t1 <- table(print_method$`4.1 What kind of printing method is used?_ae684e28-4f90-44ad-9de2-731d076de0b0_Answer`)

text(x = barplot(t1, ylab = "Number of studies", ylim=c(0, max(t1) + 10), cex.names=0.8, col = "green", main = "What kind of printing method was used?", las=0), y = t1 + 2, labels = t1, cex = 0.8 )

Figure 1b - What forms are printed with the ink?

# Data cleaning
# Those with a semicolon response ; should now be called "combo"
# responses separate with a pipe should be merged when they are saying the same thing. 
printer_forms <- reconciled %>% 
  mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "Grid|Grid" = "Grid")) %>% 
  mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "lobular liver|lobular liver" = "lobular liver")) %>% 
  mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "Other|Other" = "Other")) %>% 
  mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "Grid;Other" = "Combination")) %>% 
  mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "lobular liver;Other" = "Combination"))

t4 <-table(printer_forms$`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`)

text(x = barplot(t4, ylab = "Number of studies", ylim=c(0, max(t4) + 10), cex.names=.8 , col = "green", main = "What kind of forms are printed with this ink?"), y = t4 + 2, labels = t4, cex = 0.8)

Figure 1c - Which printing methods are used which which bioink?

Interactive sankey diagram - hover over each section to see the number of experiments in each category.

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

#### prep data for sankey diagram
# type --> 
Print_bioink <- reconciled %>%select(
  study_ID, 
  `4.1 What kind of printing method is used?_ae684e28-4f90-44ad-9de2-731d076de0b0_Answer`,
  `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`, 
  `4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`, 
  `4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`, 
  `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`, 
  `4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
)  


Print_bioink <- Print_bioink  %>% rename(
  print_method = `4.1 What kind of printing method is used?_ae684e28-4f90-44ad-9de2-731d076de0b0_Answer`,
  typeGeneral_level1 = `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`, 
  typeNatural_level2 = `4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`, 
  typeSynthetic_level2 = `4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`, 
  typeProtein_level3 = `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`, 
  typePoly_level3 = `4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
)



Print_bioink1 <- Print_bioink %>% 
                separate_rows(print_method , sep="\\|") %>% 
              separate_rows(typeGeneral_level1, sep="\\|") 

Print_bioink1$print_method <- as.factor(Print_bioink1$print_method)
Print_bioink1$typeGeneral_level1 <- as.factor(Print_bioink1$typeGeneral_level1 )
  
# summary(Print_bioink1)

Print_bioink1_sankey <- Print_bioink1 %>% select(study_ID, print_method, typeGeneral_level1)


# create a table of frequencies
freq_table <- Print_bioink1_sankey %>% group_by(print_method, typeGeneral_level1) %>% 
  summarise(n = n())

## `summarise()` has grouped output by 'print_method'. You can override using the
## `.groups` argument.

# create a nodes data frame
nodes <- data.frame(name = unique(c(as.character(freq_table$print_method),
                                    as.character(freq_table$typeGeneral_level1))))


# create links dataframe
links <- data.frame(source = match(freq_table$print_method, nodes$name) - 1,
                    target = match(freq_table$typeGeneral_level1, nodes$name) - 1,
                    value = freq_table$n,
                    stringsAsFactors = FALSE)



# Make Sankey diagram
plot_ly(
  type = "sankey",
  orientation = "h",
  node = list(pad = 15,
              thickness = 20,
              line = list(color = "black", width = 0.5),
              label = nodes$name),
  link = list(source = links$source,
              target = links$target,
              value = links$value),
  textfont = list(size = 10),
  width = 720,
  height = 480
) %>%
  layout(title = "Sankey Diagram: Print Method & Ink Type",
         font = list(size = 14),
         margin = list(t = 40, l = 10, r = 10, b = 10))

Figure 1d - What “Other” printed forms are used?

library(stringr)

print_form_comment_other <- tibble( 
   study_ID = reconciled$study_ID, 
   method =  reconciled$`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, 
   comment = reconciled$`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Comments`) 

print_form_comment_other <- print_form_comment_other %>% 
                  separate_rows(method, sep="\\|") %>%  
                  subset(method == "Other")

print_form_comment_other <- print_form_comment_other %>% 
                  separate_rows(comment, sep="\\|")

# remove line breaks
print_form_comment_other$comment <- str_replace_all(print_form_comment_other$comment, "[\n]" , "")

# remove whitespace from start of str
print_form_comment_other$comment <- str_trim(print_form_comment_other$comment, "left")

# remove whitespace from end of str
print_form_comment_other$comment <- str_trim(print_form_comment_other$comment, "right")

# turn variable into a factor to group similar
#print_form_comment_other$comment <- as.factor(print_form_comment_other$comment)

 table(print_form_comment_other$comment)

## 
## Complex,  perfusable architectures                          cruciform 
##                                  1                                  1 
##                              donut                            doplets 
##                                  4                                  1 
##                           droplets                         gear shape 
##                                  8                                  1 
##                          hexagonal               hexagonal constructs 
##                                  1                                  1 
##                          honeycomb                              lines 
##                                  1                                  3 
##                          not clear                  printed on a chip 
##                                  1                                  5 
##                 printing on a chip                          rectangle 
##                                  1                                  1 
##                         sinusoidal                           spheroid 
##                                  1                                  1 
##                          spheroids                             square 
##                                  2                                  1 
##    two-compartment planar geometry 
##                                  7

# is.data.frame(print_form_comment_other)
 
# rename answers to more easily group them
print_form_comment_other <- print_form_comment_other %>% 
      mutate(comment = recode(comment, "doplets" = "droplets")) %>% 
      mutate(comment = recode(comment, "printing on a chip" = "printed on a chip")) %>%   
     mutate(comment = recode(comment, "spheroid" = "spheroids")) %>%
     mutate(comment = recode(comment, "hexagonal constructs" = "hexagonal"))
             

# group by numbers
commentsPrintForm <- print_form_comment_other %>%  group_by(comment) %>% summarise(n_unique = length(unique(study_ID))) %>%  arrange(desc(n_unique))


#install.packages("formattable")
library(formattable)

## 
## Attaching package: 'formattable'

## The following object is masked from 'package:plotly':
## 
##     style

formattable(commentsPrintForm,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_unique`= color_bar("lightgreen")
))

comment	n_unique
droplets	6
two-compartment planar geometry	4
printed on a chip	3
spheroids	3
hexagonal	2
lines	2
Complex, perfusable architectures	1
cruciform	1
donut	1
gear shape	1
honeycomb	1
not clear	1
rectangle	1
sinusoidal	1
square	1

Figure 2 - BioInks

Figure 2a - What bioinks were used?

Interactive Sunburst plot - hover over each section to see the number of experiments in each category.

#### BIOINK sunburst 

library(plotme)

# type --> 
bioink <- reconciled %>% select(
  study_ID, 
    `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`, 
    `4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`, 
    `4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`, `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`, 
`4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
  )  


bioink <- bioink  %>% rename(typeGeneral_level1 = `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`, 
   typeNatural_level2 = `4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`, 
  typeSynthetic_level2 = `4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`, 
  typeProtein_level3 = `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`, 
  typePoly_level3 = `4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
)

bioink_split1 <- separate_rows(bioink, typeGeneral_level1 , sep="\\|")
bioink_split1_1 <- separate_rows(bioink_split1, typeNatural_level2 , sep="\\|")
bioink_split1_2 <- separate_rows(bioink_split1_1, typeProtein_level3 , sep="\\|")
bioink_split1_3 <- separate_rows(bioink_split1_2, typePoly_level3 , sep="\\|")
bioink_split2 <- separate_rows(bioink_split1_3, typeNatural_level2 , sep=";")
bioink_split3 <- separate_rows(bioink_split2, typeProtein_level3 , sep=";")
bioink_split3 <- separate_rows(bioink_split3, typePoly_level3 , sep=";")
# 148 rows
# 164 rows

test_bio <- bioink_split3 %>%
     mutate(
typeNatural_level2 = replace(typeNatural_level2, typeGeneral_level1!="Natural", NA), typeSynthetic_level2 = replace(typeSynthetic_level2, typeGeneral_level1!="Synthetic", NA), typeProtein_level3 = replace(typeProtein_level3, typeNatural_level2!="Protein based", NA), 
typeProtein_level3 = replace(typeProtein_level3, typeGeneral_level1!="Natural", NA), 
typePoly_level3 =  replace(typePoly_level3, typeNatural_level2!="Polysaccharide based", NA), 
typePoly_level3 =  replace(typePoly_level3, typeGeneral_level1!="Natural", NA)
            )


test_bio$typeSynthetic_level2 <- as.character(test_bio$typeSynthetic_level2)
  

### merge level 2 & merge level 3 
test_bio$level3 <- ifelse(!is.na(test_bio$typeProtein_level3), test_bio$typeProtein_level3, test_bio$typePoly_level3)
test_bio$level2 <- ifelse(!is.na(test_bio$typeNatural_level2), test_bio$typeNatural_level2, test_bio$typeSynthetic_level2)

  
# table(test_bio$level3)


# rename answers to more easily group them
test_bio <- test_bio %>% 
      mutate(level3 = recode(level3, "Collagens" = "Collagen")) %>% 
      mutate(level2 = recode(level2, "Poly ethylene glycol (PEG)" = "PEG"))  
# %>%      mutate(level2 = recode(level2, "Poly ethylene glycol (PEG)" = "PEG"))  
  
  

#### START PLOTTING

library(dplyr)
library(plotme)


bioInk_count <-  count(test_bio, 
                       typeGeneral_level1, 
                       level2,
                       level3
                       #,study_ID
                       )


# sunburst plot
plotme::count_to_sunburst(bioInk_count)

Figure 2b - Cell Densities

cellDensity_num <- tibble(
  study_ID = reconciled$study_ID,
  cellDensity = reconciled$`4.1.3.4.1 Please specify the cell density cells/ml!_a995234c-c94e-4d14-8a6c-fe5bd759b928_Answer`)


cellDensity_num <- separate_rows(cellDensity_num, cellDensity, sep="\\|")
cellDensity_num <- separate_rows(cellDensity_num, cellDensity, sep=";")


# remove line breaks
cellDensity_num$cellDensity <- str_replace_all(cellDensity_num$cellDensity, "[\n]" , "")

# convert to e+ for scientific notation conversion
cellDensity_num$cellDensity <- str_replace_all(cellDensity_num$cellDensity, "\\*10E" , "e+")

# remove whitespace from start & end of str
cellDensity_num$cellDensity <- str_trim(cellDensity_num$cellDensity, "left")
cellDensity_num$cellDensity <- str_trim(cellDensity_num$cellDensity, "right")


# rename answers to more easily group them
# cellDensity_num <- cellDensity_num %>% 
#       mutate(cellDensity = recode(cellDensity, "1*10E6" = "microarray spotter")) %>% mutate(cellDensity = recode(cellDensity, "scaffold free bioprinting" = "scaffold-free bioprinting"))


cellDensity_num$cellDensity <- as.numeric(cellDensity_num$cellDensity)

## Warning: NAs introduced by coercion

hist(cellDensity_num$cellDensity, breaks = 50)

Figure 2c - Origin of BioInks

t8 <- table(reconciled$`4.1.3.2. What is the origin of the bioink?_34ef46b6-34ac-4ddb-a5de-07bc74272fca_Answer`)

bioOrigin <-  reconciled %>%select(
  study_ID, 
    `4.1.3.2. What is the origin of the bioink?_34ef46b6-34ac-4ddb-a5de-07bc74272fca_Answer`
 #, `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`
  ) %>%  separate_rows(`4.1.3.2. What is the origin of the bioink?_34ef46b6-34ac-4ddb-a5de-07bc74272fca_Answer`, sep="\\|")

t8 <- table(bioOrigin$`4.1.3.2. What is the origin of the bioink?_34ef46b6-34ac-4ddb-a5de-07bc74272fca_Answer`)

text(x = barplot(t8, ylab = "Number of studies", ylim=c(0, max(t8) + 10), cex.names=.8 , col = "blue", main = "What is the origin of BioInk"), y = t8 + 2, labels = t8, cex = 0.8)

Figure 2d - Available Meta-data for BioInks

Frequency of reporting and what meta-data was actually reported.

# Maren comment: I think what was reported how often would be most interesting. e.g., 10 out of 34 mentioned the concentration, etc
# 4.1.3.2.3.

custom_bioInk <- tibble( 
   study_ID = reconciled$study_ID, 
   infoProivded =  reconciled$`4.1.3.2.3 If custom formulated, which information is provided for the bioink?_fe62b3e4-d8f1-4584-a90d-e07317e4c8e1_Answer`
 # , comment = reconciled$`4.1.3.2.3 If custom formulated, which information is provided for the bioink?_fe62b3e4-d8f1-4584-a90d-e07317e4c8e1_Comments`
 ) 


custom_bioInk <- separate_rows(custom_bioInk, infoProivded, sep="\\|")
custom_bioInk <- separate_rows(custom_bioInk, infoProivded, sep=";")

# group by numbers
custom_bioInk_count <- custom_bioInk %>%  group_by(infoProivded) %>% summarise(n_studies_report = length(unique(study_ID))) %>%  arrange(desc(n_studies_report))

library(formattable)

formattable(custom_bioInk_count,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_studies_report`= color_bar("lightblue")
))

infoProivded	n_studies_report
Concentration	51
Composition	50
Solvent	42
NA	11
Storage conditions	7
None	1

#################################
####### How many items were reported? 

custom_bioInk_studies <- custom_bioInk %>%  group_by(study_ID) %>% summarise(n_items_reported = length(unique(infoProivded))) %>%  arrange(desc(n_items_reported)) 

# hist(custom_bioInk_studies$n_items_reported)

t11 <- table(custom_bioInk_studies$n_items_reported)

#barplot(table(custom_bioInk_studies$n_items_reported), ylab = "Number of studies", cex.names=.7, col = "lightblue")

text(x = barplot(t11, ylab = "Number of studies", ylim=c(0, max(t11) + 10), cex.names=.8 , col = "lightblue", main = "How many items were reported about custom-formulated bioink?"), y = t11 + 2, labels = t11, cex = 0.8)

Figure 3 - Liver Cells

Figure 3a - What Type of Liver Cells are used?

### combine the ones with semi-colon - into a new category called "Combination"

liver_cells <- reconciled %>% 
  mutate(`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer` = recode(`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`, "Hepatoma cells;Other" = "Combination")) %>% 
   mutate(`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer` = recode(`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`, "Primary cells;Other" = "Combination")) 

t9 <- table(liver_cells$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`)

#barplot(table(reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`), ylab = "Number of studies", cex.names=.5, col = "turquoise")

text(x = barplot(t9, ylab = "Number of studies", ylim=c(0, max(t9) + 10), cex.names=.5 , col = "turquoise", main = "What type of Liver Cells are included?"), y = t9 + 2, labels = t8, cex = 0.8, )

Figure 3b - What cell cultures were used?

liverType <- tibble(
  study_ID = reconciled$study_ID, 
  liverCells = reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`, 
  liverCellsComment = reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Comments`
)


# remove line breaks
library(stringr)
liverType$liverCellsComment <- str_replace_all(liverType$liverCellsComment, "[\n]" , "")
liverType <- separate_rows(liverType, liverCellsComment, sep=";")

# remove whitespace from start of str
liverType$liverCellsComment <- str_trim(liverType$liverCellsComment, "left")

# remove whitespace from end of str
liverType$liverCellsComment <- str_trim(liverType$liverCellsComment, "right")

liverType <- liverType %>% 
      mutate(liverCellsComment = recode(liverCellsComment, "Human iPSC-derived hepatocytes" = "human hepatocytes")) %>% 
      mutate(liverCellsComment = recode(liverCellsComment, "adult stem cell derived hepatocytes" = "human hepatocytes")) %>% 
  mutate(liverCellsComment = recode(liverCellsComment, "cholangiocarinoma cells" = "human cells - other")) %>% 
  mutate(liverCellsComment = recode(liverCellsComment, "bone marrow mesenchymal cells" = "human cells - other")) %>% 
  mutate(liverCellsComment = recode(liverCellsComment, "hepatic progenitor cells" = "human cells - other")) %>% 
  mutate(liverCellsComment = recode(liverCellsComment, "hepatic stellate cells" = "human cells - other")) %>% 
  mutate(liverCellsComment = recode(liverCellsComment, "Hepatocellular carcinoma" = "human cells - other")) %>% 
    mutate(liverCellsComment = recode(liverCellsComment, "aHSC" = "human cells - other")) %>% 
    mutate(liverCellsComment = recode(liverCellsComment, "AML12" = "human cells - other")) %>% 
    mutate(liverCellsComment = recode(liverCellsComment, "hiHep" = "human cells - other")) %>% 
   mutate(liverCellsComment = recode(liverCellsComment, "hESC lines RC-6 and RC-10" = "human cells - other")) %>% 
  mutate(liverCellsComment = recode(liverCellsComment, "HMCS1SA" = "human cells - other")) %>% 
   mutate(liverCellsComment = recode(liverCellsComment, "liver biopsies" = "human liver tissue")) %>% 
   mutate(liverCellsComment = recode(liverCellsComment, "porcine liver tissue" = "porcine liver tissue"))

# liverType$liverCellsComment




# create categories
liverType$liverCellsComment <- as.factor(liverType$liverCellsComment)

commentsLiver <- liverType %>%  group_by(liverCellsComment) %>% summarise(n_unique = length(unique(study_ID))) %>%  arrange(desc(n_unique))


## remove NA options 
# remove NAs as they dont make sense
commentsLiver <- commentsLiver %>%  
                    drop_na(liverCellsComment)


#install.packages("formattable")
library(formattable)

formattable(commentsLiver,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_unique`= color_bar("turquoise")
))

liverCellsComment	n_unique
HepG2	26
human hepatocytes	10
human cells - other	7
HepaRG	5
murine hepatocytes	4
Hep3B	3
Huh7	2
LX2	2
hESCs (RC-6, RC-10)	1
Human hepatocytes	1
human liver tissue	1
miHeps (mouse hepatocyte-like cells)	1
porcine liver tissue	1

Figure 3c - What Type of non-parenchymal cells were included in the co-cultures?

nonParanchymal <- tibble(
  study_ID = reconciled$study_ID, 
  nonParanchymalCellType = reconciled$`2.2.1.1 If co-cultured, what type of non-parenchymal cells are included?_b729a62f-2dc7-47b6-ada7-e1ed8e363918_Answer`, 
  Comment = reconciled$`2.2.1.1 If co-cultured, what type of non-parenchymal cells are included?_b729a62f-2dc7-47b6-ada7-e1ed8e363918_Comments`
)

nonParanchymal <- separate_rows(nonParanchymal, nonParanchymalCellType, sep="\\|")
nonParanchymal <- separate_rows(nonParanchymal, nonParanchymalCellType, sep=";")


# group by numbers
nonParanchymal_count <- nonParanchymal %>%  group_by(nonParanchymalCellType) %>% summarise(n_studies_report = length(unique(study_ID))) %>%  arrange(desc(n_studies_report))

library(formattable)

formattable(nonParanchymal_count,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_studies_report`= color_bar("turquoise")
))

nonParanchymalCellType	n_studies_report
NA	30
Endothelial cells	19
Other	16
Hepatic stellate cells	10
Immune cells	3

Figure 3d - What Meta-data was provided for the Liver Cells?

liverCell_meta <- reconciled %>% mutate(`2.4 Which kind of meta data is available for the used liver cells?_2868ee50-2c4c-47e8-a040-427e4f882632_Answer` = recode(`2.4 Which kind of meta data is available for the used liver cells?_2868ee50-2c4c-47e8-a040-427e4f882632_Answer`, "Common cell line;None" = "Common cell line"))

liverMetaData <- separate_rows(liverCell_meta, `2.4 Which kind of meta data is available for the used liver cells?_2868ee50-2c4c-47e8-a040-427e4f882632_Answer` ,sep=";")


liverMetaData_heat <- liverMetaData[,c(1, 58)]

colnames(liverMetaData_heat) <- c("study_ID", "metaData")

countsMetaDat <- liverMetaData_heat %>% group_by(study_ID) %>% summarize(n_unique = length(unique(metaData)))

t10 <- table(countsMetaDat$n_unique)
#  1  2  3 
# 56  2  5 


hist(countsMetaDat$n_unique, xlab = "Number of Meta-Data Items Reported", ylab = "Number of Papers", main = "Meta-Data Items about the Cell Lines Peformed per Paper", breaks = 3)

## convert to facotrs to display barplot
 countsMetaDat$n_unique <- as.factor(countsMetaDat$n_unique)


text(x = barplot(t10, ylab = "Number of Meta-Data Items", ylim=c(0, max(t10) + 10), cex.names=.8 , col = "turquoise", main = "How many meta-data items are reported about the liver cells?"), y = t10 + 2, labels = t10, cex = 0.8)

Figure 3e - Additives

### Additives
# -- needs cleaning 
# table(reconciled$`4.1.3.3 Which information is provided on the additives in the ink or culture? _4c2b5908-9c3c-4ba8-91f2-dd9771ac2ad4_Answer`)
# needs cleaning

liverAdditive <- tibble(
  study_ID = reconciled$study_ID, 
  additive = reconciled$`4.1.3.3 Which information is provided on the additives in the ink or culture? _4c2b5908-9c3c-4ba8-91f2-dd9771ac2ad4_Answer`
)


liverAdditive <- separate_rows(liverAdditive, additive, sep="\\|")
liverAdditive <- separate_rows(liverAdditive, additive, sep=";")

# group by numbers
liverAdditive_count <- liverAdditive %>%  group_by(additive) %>% summarise(n_studies_report = length(unique(study_ID))) %>%  arrange(desc(n_studies_report))

library(formattable)

formattable(liverAdditive_count,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_studies_report`= color_bar("turquoise")
))

additive	n_studies_report
Concentration	50
Manufacturer	42
None	13
Order number	10

#################################
####### How many items were reported? 

additives_studies <- liverAdditive %>%  group_by(study_ID) %>% summarise(n_items_reported = length(unique(additive))) %>%  arrange(desc(n_items_reported)) 

# hist(custom_bioInk_studies$n_items_reported)

t12 <- table(additives_studies$n_items_reported)

#barplot(table(custom_bioInk_studies$n_items_reported), ylab = "Number of studies", cex.names=.7, col = "lightblue")

text(x = barplot(t12, ylab = "Number of studies", ylim=c(0, max(t12) + 10), cex.names=.8 , col = "turquoise", main = "How many items were reported about bioink or co-culture additives?"), y = t12 + 2, labels = t12, cex = 0.8)

###################################################
## 2.1.1.1 - if xeno free

additives <- tibble(
  study_ID = reconciled$study_ID, 
  additive = reconciled$`2.1.1.1 If not xeno-free, which additives are included in the study?_39f4070b-a7cd-4b06-9618-47e07363219c_Answer`
)

additives <- separate_rows(additives, additive, sep="\\|")
additives <- separate_rows(additives, additive, sep=";")


# group by numbers
Additive_count <- additives %>%  group_by(additive) %>% summarise(n_studies_report = length(unique(study_ID))) %>%  arrange(desc(n_studies_report))

library(formattable)

formattable(Additive_count,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_studies_report`= color_bar("turquoise")
))

additive	n_studies_report
Fetal Bovine Serum (FBS)	37
Gelatin	27
NA	20
Other	12
Matrigel	9
Bovine Serum Albumin (BSA)	8
Collagen	8
Basement Membrane Extracts (BME)	1

#### 2.1.2 - Animal 

additivesAnimal <- tibble(
  study_ID = reconciled$study_ID, 
  additive = reconciled$`2.1.2 If animal, which of the following additives are included in the study?_bd00875c-4d61-4449-8629-6ff8fab20ec5_Answer`
)



additivesAnimal <- separate_rows(additivesAnimal, additive, sep="\\|")
additivesAnimal <- separate_rows(additivesAnimal, additive, sep=";")


# group by numbers
AdditiveAnimal_count <- additivesAnimal %>%  group_by(additive) %>% summarise(n_studies_report = length(unique(study_ID))) %>%  arrange(desc(n_studies_report))

library(formattable)

formattable(AdditiveAnimal_count,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_studies_report`= color_bar("turquoise")
))

additive	n_studies_report
NA	59
Fetal Bovine Serum (FBS)	3
Collagen	1
Gelatin	1
Not reported	1
Other	1

#### 2.1.3 - if both
additivesBoth <- tibble(
  study_ID = reconciled$study_ID, 
  additive = reconciled$`2.1.3 If both, which of the following additives are included in the study?_648859a8-3da6-4d7e-b457-d3443a608681_Answer`
)

additivesBoth <- separate_rows(additivesBoth, additive, sep="\\|")
additivesBoth <- separate_rows(additivesBoth, additive, sep=";")


# group by numbers
AdditiveBoth_count <- additivesBoth %>%  group_by(additive) %>% summarise(n_studies_report = length(unique(study_ID))) %>%  arrange(desc(n_studies_report))

library(formattable)

formattable(AdditiveBoth_count,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_studies_report`= color_bar("turquoise")
))

additive	n_studies_report
NA	49
Fetal Bovine Serum (FBS)	14
Gelatin	5
Bovine Serum Albumin (BSA)	2
Collagen	2
Matrigel	1
Other	1

Figure 4 - Model Conditions and Measurements

Figure 4a

t13 <- table(reconciled$`5.2 How long were the liver models cultured after printing?_1f136f0a-786b-4e17-a97d-e5cf32f47b26_Answer`)
        # < 72 hours 2 weeks - 3 months   3 days - 2 weeks       not reported 
        #          2                 16                 42                  3 

# manually set levels 

# barplot(table(reconciled$`5.2 How long were the liver models cultured after printing?_1f136f0a-786b-4e17-a97d-e5cf32f47b26_Answer`), ylab = "Number of studies", cex.names=.5, col = "purple")


text(x = barplot(t13, ylab = "Number of Studies", ylim=c(0, max(t13) + 10), cex.names=.8 , col = "purple", main = "How long were liver models cultured?"), y = t13 + 2, labels = t13, cex = 0.8)

Figure 4b- What Liver Proteins were measured?

# otherLiverMetabolite <- liverMetabolite_comments %>%  group_by(`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments`) %>% summarise(n_unique = length(unique(study_ID))) %>%  arrange(desc(n_unique))

# otherLiverMetabolite <- otherLiverMetabolite %>% 
#   drop_na(`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments`)


liverMetabolite <- tibble(
  study_ID = reconciled$study_ID, 
  metabolite = reconciled$`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Answer`, 
  comment = reconciled$`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments`
) 


liverMetabolite <- separate_rows(liverMetabolite, metabolite, sep="\\|")
liverMetabolite <- separate_rows(liverMetabolite, metabolite, sep=";")


liverMetabolite <- liverMetabolite %>%  group_by(metabolite) %>% summarise(n_unique = length(unique(study_ID))) %>%  arrange(desc(n_unique))


# remove NAs as they dont make sense
liverMetabolite <- liverMetabolite[liverMetabolite$metabolite != "None" ,]


#install.packages("formattable")
library(formattable)

formattable(liverMetabolite,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_unique`= color_bar("yellow")
))

metabolite	n_unique
Albumin	47
Urea	24
Other	7
Bile acid	3

#### CLEAN THIS!! 
# formattable(otherLiverMetabolite, 
#              align = c("l", "r"), 
#              list(`Indicator Name` = formatter(
#               "span", style = ~ style(color = "grey",font.weight = "bold")), 
#               `n_unique`= color_bar("yellow")
#             
#             ))

Figure 4c - What Cytochome Isoforms were measured?

# liverCytochome <- separate_rows(reconciled, `6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Answer`
#    ,sep=";")

liverCytochome_only <- tibble(
  study_ID = reconciled$study_ID, 
  cytochrome = reconciled$`6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Answer`
) 


liverCytochome_only <- separate_rows(liverCytochome_only, cytochrome, sep="\\|")
liverCytochome_only <- separate_rows(liverCytochome_only, cytochrome, sep=";")


liverCytochome_only <- liverCytochome_only %>%  group_by(cytochrome) %>% summarise(n_unique = length(unique(study_ID))) %>%  arrange(desc(n_unique))


# remove NAs as they dont make sense
liverCytochome_only <- liverCytochome_only %>%  
                    drop_na(cytochrome)

formattable(liverCytochome_only,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_unique`= color_bar("yellow")
))

cytochrome	n_unique
CYP3A	21
CYP1A	15
CYP2B	5
CYP2C	5
CYP2D	5
CYP2E	4
Not reported	2
CYP1A2	1
YP2C	1

Figure 4d - Storage Conditions

t14 <- table(reconciled$`5.1 Does the study report the storage conditions of the printed models?_0adebee3-f3bc-4b01-a756-f8a93dc7b5bc_Answer`)


text(x = barplot(t14, ylab = "Number of Studies", ylim=c(0, max(t14) + 10), cex.names=.8 , col = "purple", main = "Were storage conditions reported?"), y = t14 + 2, labels = t14, cex = 0.8)

Figure 4d - Frequency of Assays Used

# 5.3.1

assays <- tibble(
  study_ID = reconciled$study_ID, 
  assay = reconciled$`5.3.1 Which assays were performed to assure the quality of the liver model?_8a415412-28c6-48ff-840a-538ea69a068f_Answer`
) 

assays <- separate_rows(assays, assay, sep="\\|")
assays <- separate_rows(assays, assay, sep=";")


assays <- assays %>%  group_by(assay) %>% summarise(n_unique = length(unique(study_ID))) %>%  arrange(desc(n_unique))


# remove NAs as they dont make sense
assays <- assays %>%  
                    drop_na(assay)


customPurple = "#C5B4E3"

formattable(assays,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_unique`= color_bar(customPurple)
))

assay	n_unique
Viability test	47
Histological characterization	45
Live/Dead Cell Staining	44
Enzyme linked immunosorbent Assay (ELISA) of liver markers	30
Real-time quantitative PCR of liver markers	28
Rheological test	23
Mechanical stiffness	15
Size measurement	13
Biodegradation	4
Biocompatibility	3

Figure 4e - Agonists

# 6.3.1 


agonists <- tibble(
  study_ID = reconciled$study_ID, 
  agonist = reconciled$`6.3.1  Please specify the agonists of the receptors that are applied._d42bd778-8656-4e6b-8b11-e4bdecb6a9b9_Answer`, 
  agonistsComment = reconciled$`6.3.1  Please specify the agonists of the receptors that are applied._d42bd778-8656-4e6b-8b11-e4bdecb6a9b9_Comments`
)


agonists <- separate_rows(agonists, agonist, sep="\\|")
agonists <- separate_rows(agonists, agonist, sep=";")


agonists <- separate_rows(agonists, agonistsComment, sep="\\|")
agonists <- separate_rows(agonists, agonistsComment, sep=";")

# remove line breaks
agonists$agonistsComment <- str_replace_all(agonists$agonistsComment, "[\n]" , "")

agonists_count <- agonists %>%  group_by(agonist) %>% summarise(n_unique = length(unique(study_ID))) %>%  arrange(desc(n_unique))


# remove NAs/None as they dont make sense
# liverMetabolite <- liverMetabolite[liverMetabolite$metabolite != "None" ,]


#install.packages("formattable")
library(formattable)

customPurple = "#C5B4E3"

formattable(agonists_count,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_unique`= color_bar(customPurple)
))

agonist	n_unique
NA	55
Other	7
Pregnane X receptor (PXR)	1

table(agonists$agonistsComment)

## 
##                Acetaminophen      Omeprazole  Phenobarbital     Rifampicin 
##              1              1              2              1              7

Figure 5 - Applications

# Application 
t15 <- table(reconciled$`7.1 Do the authors apply the model in the study?_a6895d38-a9ed-4605-a092-4f06ebbd9e2b_Answer`)
 # No Yes 
 # 35  28 



text(x = barplot(t15, ylab = "Number of Studies", ylim=c(0, max(t15) + 10), cex.names=.8 , col = "gray", main = "Do authors apply the model in the study?"), y = t15 + 2, labels = t15, cex = 0.8)

###########################################################
# field of application 
#table(reconciled$`7.1.1 please select the field of application. _d332c2ee-9864-434f-8595-90305808e32d_Answer`)


applications <- tibble(
  study_ID = reconciled$study_ID, 
  application = reconciled$`7.1.1 please select the field of application. _d332c2ee-9864-434f-8595-90305808e32d_Answer`
)


applications <- separate_rows(applications, application, sep="\\|")
applications <- separate_rows(applications, application, sep=";")

applications_count <- applications %>%  group_by(application) %>% summarise(n_unique = length(unique(study_ID))) %>%  arrange(desc(n_unique))


# remove NAs/None as they dont make sense
# liverMetabolite <- liverMetabolite[liverMetabolite$metabolite != "None" ,]
applications_count <- applications_count %>%  
                    drop_na(application)

#install.packages("formattable")
library(formattable)

customPurple = "#C5B4E3"

formattable(applications_count,
            align =c("l", "r"),
            list(`Indicator Name` = formatter(
              "span", style = ~ style(color = "grey",font.weight = "bold")), 
              `n_unique`= color_bar("lightgrey")
))

application	n_unique
Toxicity testing	15
Drug dosage testing	9
Disease modeling	5
Other	4
Xenograft (implantation into animal)	3
Implant / Medical surgery	1

Supplementary Figures

Printer Forms Over Times

printer_form_year<- table(printer_forms$`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, printer_forms$NA_Year)


printer_form_yearDF <- as.data.frame(printer_form_year)
# long to wide 
printer_form_yearDF <- printer_form_yearDF %>% spread(
   key = Var2, 
   value = Freq)


# printer_form_yearDF 
# names(printer_form_yearDF) <- gsub(x = names(printer_form_yearDF), pattern = "X", replacement = "")  


library(formattable)

formattable(printer_form_yearDF,
            #align =c("l", "r"),
            list(
              `Indicator Name` = formatter(
              "span", 
              style = ~ style(color = "grey",font.weight = "bold")), 
             area(row = 1:4) ~ color_tile("white", "green")))

## Warning in gradient(as.numeric(x), ...): NAs introduced by coercion

Var1	2010	2011	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Grid	0	0	0	1	0	3	4	4	1	5	5	0
lobular liver	0	0	0	0	0	1	0	3	1	2	2	1
Other	1	1	1	0	1	4	2	6	3	5	2	3
Toroids	0	0	0	0	1	0	0	0	0	0	0	0